In-Depth Prosecution Stats – 2(d) Refusals in Class 1

At TM TKO, we are really interested in prosecution trends. Today, we are going to zoom in on one type of refusal – 2(d) likelihood of confusion refusals – in one class, International Class 1. International Class 1 primarily consists of industrial chemicals and similar products.

In particular, we wanted to test out class relationships – how strong was the likelihood of getting a refusal if the prior mark was in-class, was in a coordinated class (but not in-class), or was in an “unrelated” class (not in-class and not in a coordinated class), and how likely were the refusals to “stick” and cause the application to go abandoned?

While fewer than 12% of applications filed at any time received 2(d) citations, the 2(d) citation rate jumps to about 30% from 2014 to the present. It’s perhaps unsurprising that an increasing 2(d) rate results from an increasingly crowded registry.

As for the content of the cited marks, 2(d) refusals were not quite twice as common in the same class (i.e. the prior registration had International Class 1) than in all the classes noted by the Office as “coordinated” classes combined (any of International Classes 5, 17, 35, 42, 44, A, or B). “Out-of-class” citations – all other classes except Class 1 or any coordinated classes – were surprisingly common, with slightly more “out-of-class” citations than in-class citations, and about twice the number of out-of-class citations as coordinated class citations. There are many more marks not in one of Class 1’s coordinated classes than not, of course, which may account for the difference – currently, there are some 67,000 active filings in Class 1, just short of 950,000 in Class 1 or a coordinated class of Class 1, and more than three million neither in Class 1 nor a coordinated class of Class 1.

Cl1_2d_core

The importance of class and coordinated class are more apparent in looking at how frequency applicants overcome the initial 2(d) citation or citations. Applicants that receive a 2(d) citation in Class 1 or a coordinated class overcome it roughly 60% of the time; applicants that receive a 2(d) citation in an “unrelated” class are about 10% more likely to be successful. The same trends hold since 2014 as over all citations, suggesting that – at least for this class – citations in unrelated classes were noticeably less likely to hold up after further argument by the applicant.

Cl1_2d_coor

Within the coordinated classes, refusals in Class 1 tended to be the most difficult to overcome; citations to Class 42 (traditionally a grab-bag of services, though now more narrowly defined) and Class 200 were the most likely to be overcome by applicants. Refusals in Classes A and B were less common, but comparatively difficult to overcome, perhaps because they can include especially broad descriptions of goods.

We will be looking at other classes and other prosecution in more depth over the coming weeks and months. If you have specific topics of interest, please let us know at inquiries@tmtko.com.

Mark Length and Your Searching

TM TKO just added a new manual search field – mark length. What can you do with this?

Let’s say you have a client with a two-letter mark – an acronym for their company name. Let’s say your client is a bank – Penny & Associates. It wants to register its PA (stylized) mark. A prior registration for PA (stylized) is owned by Pennsylvania Accountants, Inc. – but with a pretty different stylization. Before you file, you want to see if there are any other examples of co-existence for these same services with other two-letter marks.

With TM TKO, you can find co-existence and find examples of applicants for conceptually similar two-letter marks who faced a refusal — and overcame it.

First things first – let’s find co-existence examples. We’ll search for two-letter terms for “banking” services in Class 36, and limit to use-based registrations (to show actual marketplace overlap). We get 150 different matching results. Sort by mark, so we bring identical marks together, regardless of owner. It’s then simple to tag the overlap. We get a number of examples of co-existence: AB (3 different owners), BB (3), BC (2), C1 (2), CB (8), CC (2), FP (2), FS (3), L1 (2), MM (3), MW (2), PB (3), SC (2), SP (2), SS (2), and UN (2).

2char

See the results:

2ltrresults

You can export to Excel or Word, or grab current status and title copies with one click via the TSDR export button to provide your Examiner evidence of the overlap.

If you do the same search for Office Actions (add a “Cited Trademark Criteria” to limit to 2-letter cited marks and an “Office Action Criteria” and the “Issue” as “2(d)”), and you can use a similar search strategy to find other applicants who have overcome similar problems.

Happy researching!

Update – CSV Exports from All Tools!

TM TKO has allowed direct exports of CSV files from watch via email for several months. By popular request, we have expanded that feature across our toolset.

You can now export results from CSV for: knockout results, manual search results, watch results, and Office Action research results. Just look to your usual “Export” selection list in the top right of your results.

csv_export

We hope you enjoy this added flexibility.

Using TM TKO for External Business Development

TM TKO’s watch tools are a great way to find new clients, not just to protect your existing clients. Let’s walk through how we can find unrepresented applicants in your area facing complex prosecution problems. Let’s say you are based in Birmingham, AL, and you want to target in-state, unrepresented applicants facing Office Actions.

Go to “Watch” then “New Watch” then “Office Action.” Give your watch a name, like “Alabama Biz Dev Watch.” Pick the frequency you want updates – we will do “Daily” for this example.

Under “Trademark Critera,” select “Attorney Representation” and “No.” This will remove applications that have counsel of record listed. (Some in-house counsel do not use this field, so be sure to take a look.) Click “Add Rule” and select “Owner Addresses,” and add “any” of “AL” or “Alabama.”

Leave “Cited Trademark Criteria” blank.

Under “Office Action Criteria,” add “Direction” and select “Outgoing (PTO)” to see new outbound Office Actions. Then “Add Rule” and add “Issue Type” if you want to limit to only certain types of Office Action content.

If you plan on using a mail merge as a part of your outreach, select “Include CSV results file with notification” and you will get an Excel-style spreadsheet that’s simple to use for custom communications.

A screenshot showing a representative custom watch is below. You can do all sorts of issue targeting, geotargeting, or even focus on upcoming post-registration deadlines using a similar “Trademark” watch – there’s no limit to the opportunities you can uncover.

Of course, be sure to follow your local jurisdiction’s rules about attorney advertising. Good luck finding new clients and expanding your business!

AL_Biz_Dev

Dev Blog – Tech Upgrades and New Features

TM TKO has recently rolled out a significant upgrade to its back end platform. The changes should significantly speed up certain types of searches, especially certain types of manual searches and watches. The new back-end also opens up a lot of options for us from a development perspective.

The first “front-end” upgrade available as a result of these back-end changes is the ability to sort the results of all new manual searches, including via relevance, mark, mark length, mark word count, filing, publication, registration, abandonment, and status dates, attorney, correspondent firm, owner, Examiner, and law office. Find this under “Sort by” in the upper-right corner of your manual search results table. This should be a nice quality-of-life enhancement for your day-to-day searching and diligence projects!

sort

We’re looking forward to all the new enhancements and tools that we can now develop more easily with these back-end upgrades.

Weak Marks and Disclaimed Terms – The Lesser Side of Trademark Life

Trademark law views marks on a continuum of strength – the strongest marks are coined or arbitrary marks, followed by suggestive marks, with descriptive marks and then unprotectable generic terms bringing up the rear. While a strong, enforceable mark is ideal, brand owners often desire marks towards the suggestive or descriptive side of the spectrum to make it resonate more with consumers.

We at TM TKO were curious whether certain types of industries found more value in weaker marks than others. Accordingly, we did some research as to how common disclaimers and Supplemental Register registrations were among active applications and registrations in various classes.

Full data is at the bottom of this blog post.

Goods vs. Services

Both disclaimers and Supplemental Register registrations were more common for services than goods, with 21% disclaimers in goods vs. 35% in services and 3% disclaimers for goods vs. 4% disclaimers for services.

Disclaimer Trends

Among goods, disclaimer volume was fairly evenly distributed. Classes 3, 16, 19 had rates over 20%; the 29, 30, 31, and 32 all had very high rates of over 30%. Perhaps foods and drinks find more value than most industries in including the generic term for the product in addition to the distinctive part of the brand; the higher levels of disclaimers is a clear trend.

Among services, disclaimers were considerably more common. Classes 35, 36, 37, 41, and 44 all exceeded 30%, and Class 43 (restaurants and hotels) had a whopping 44% disclaimer rate – apparently, the pressing biological needs for food and drink and rest make identifying the nature of the establishment more central than these than for other services. It certainly correlates with the trends for food and drink goods.

Supplemental Register Trends

The largest Supplemental Register percentages were in Classes 16 (books and other printed matter), 35 (retail and a bunch more), 36 (insurance and financial services) 44 (medical services), 45 (legal, security, and social services), and Class B (services certification marks). Class 16 also appeared under the most common disclaimers, perhaps reflecting a trend in magazine titles or educational material titles that are more likely to find value in descriptive terms or marks?

Supplemental Register registrations were quite rare in Class 1 (chemicals), 18 (leather goods), 23 (yarn), and 24 (textiles). It’s not clear to us why admittedly descriptive marks would be more common for these sorts of products, but between leather, yarn, and textiles, they are related goods, so appears to be a trend for these related industries. Disclaimers were pretty low in each of those classes, too, so it appears to be a real trend.

Doing These Searches in TM TKO

Go to “Search,” the manual search section, then use the “Register” option (to narrow to Supplemental) or the “Disclaimer Present” option (to find those that have a disclaimer of some sort), plus class and “Lifecycle Status” set to “active.”

Data

The full data follows. Keep in mind that the numbers reflect the number of active filings that contain a class.

Class Disclaimers Disc. % Supplemental Supp. % Total
1 9,680 14% 967 1% 66,952
2 3,436 17% 357 2% 20,070
3 31,130 25% 2,629 2% 125,425
4 4,225 21% 344 2% 20,487
5 27,146 20% 3,032 2% 137,096
6 9,991 19% 1,133 2% 52,652
7 14,607 17% 1,749 2% 87,053
8 6,076 19% 692 2% 32,836
9 76,444 17% 10,890 2% 458,915
10 12,195 15% 1,776 2% 79,881
11 14,330 17% 1,891 2% 85,820
12 10,875 17% 1,114 2% 62,560
13 2,527 19% 319 2% 13,313
14 9,996 17% 1,203 2% 58,456
15 1,476 14% 287 3% 10,657
16 42,451 26% 5,946 4% 162,680
17 4,155 14% 499 2% 29,456
18 9,930 15% 765 1% 65,283
19 8,002 22% 858 2% 36,811
20 14,597 21% 1,580 2% 70,427
21 16,513 20% 1,889 2% 81,957
22 2,260 17% 250 2% 13,160
23 555 11% 40 1% 4,961
24 7,473 19% 578 1% 38,773
25 47,560 17% 6,565 2% 274,732
26 3,407 18% 389 2% 18,494
27 2,355 17% 217 2% 14,068
28 23,434 21% 2,224 2% 114,245
29 23,184 34% 1,902 3% 68,707
30 39,942 36% 3,030 3% 111,549
31 11,409 30% 995 3% 38,162
32 18,775 33% 1,297 2% 57,606
33 16,544 25% 1,386 2% 66,419
34 4,337 19% 506 2% 22,429
35 126,257 33% 14,360 4% 387,959
36 63,741 38% 6,010 4% 166,369
37 29,557 35% 2,224 3% 83,478
38 11,982 22% 1,390 3% 55,426
39 19,598 36% 1,592 3% 55,071
40 13,687 31% 1,069 2% 43,939
41 125,618 34% 14,893 4% 367,443
42 68,428 25% 8,640 3% 277,737
43 39,419 44% 2,317 3% 90,458
44 35,277 37% 3,928 4% 94,184
45 19,117 29% 2,509 4% 65,499
A 986 25% 30 1% 3,928
B 1,563 29% 245 5% 5,392
200 1,458 26% 83 1% 5,599
Goods 439,660 21% 53,831 3% 2,052,927
Services 446,962 35% 50,537 4% 1,295,357

Trademark Trends – State Names

Geographic names play an important part in branding, from creating a strong local or regional flair to a mark. As a result, there are several provisions of the Lanham Act that restrict registration to certain types of geographic marks. This post will not dive into the legal details, though – it’s looking at a broader question of popularity. Geographic names often reflect broad cultural significance, like a country name, or very local importance, like city or county or even neighborhood names. This post looks at the popularity of brands that include state names as a part of the mark or pseudo mark.

There is roughly one active trademark application or registration per every 100 US residents, so we’ll use US population vs. state population to find the “expected” ratio of trademark filings related to that state versus the actual numbers. So, we can find which states are most over-represented from a branding perspective, and what other not rigorously-statistically-supported insights we can pull from this data without this blog post taking way too long to complete.

New York did really well, but almost certainly because the city name is boosting the “state” numbers by a ton. Texas is a bit over-represented; no surprise, given the extent of “patriotism” – there is a hole in English, or at least in my personal lexicon, for a state-level equivalent of “patriotism.”

States with multiple larger cities tended to perform worse than comparably sized states with only one major city; it’s likely that city-level affiliations carry more weight in, say, Ohio than in Georgia.

Some tiny states, Hawaii, Alaska, Maine, and Vermont, were really over-represented. At least from a trademark filing perspective, people love those states! In general, smaller states tended to over-perform their expected filings; perhaps the absence of an “alternative affiliation” like a metro center or centers pushes more geographic branding towards a state name.

State Population Live Apps + Regs Expected Actual Difference
California 39,776,830 2,593 0.121 0.102 -0.020
Texas 28,704,330 2,399 0.088 0.094 0.007
Florida 21,312,211 1,346 0.065 0.053 -0.012
New York 19,862,512 3,495 0.061 0.137 0.077
Pennsylvania 12,823,989 318 0.039 0.012 -0.027
Illinois 12,768,320 327 0.039 0.013 -0.026
Ohio 11,694,664 403 0.036 0.016 -0.020
Georgia 10,545,138 612 0.032 0.024 -0.008
North Carolina 10,390,149 245 0.032 0.010 -0.022
Michigan 9,991,177 555 0.030 0.022 -0.009
New Jersey 9,032,872 285 0.028 0.011 -0.016
Virginia 8,525,660 759 0.026 0.030 0.004
Washington 7,530,552 600 0.023 0.024 0.001
Arizona 7,123,898 511 0.022 0.020 -0.002
Massachusetts 6,895,917 451 0.021 0.018 -0.003
Tennessee 6,782,564 398 0.021 0.016 -0.005
Indiana 6,699,629 352 0.020 0.014 -0.007
Missouri 6,135,888 220 0.019 0.009 -0.010
Maryland 6,079,602 195 0.019 0.008 -0.011
Wisconsin 5,818,049 362 0.018 0.014 -0.004
Colorado 5,684,203 724 0.017 0.028 0.011
Minnesota 5,628,162 393 0.017 0.015 -0.002
South Carolina 5,088,916 164 0.016 0.006 -0.009
Alabama 4,888,949 235 0.015 0.009 -0.006
Louisiana 4,682,509 284 0.014 0.011 -0.003
Kentucky 4,472,265 420 0.014 0.016 0.003
Oregon 4,199,563 431 0.013 0.017 0.004
Oklahoma 3,940,521 240 0.012 0.009 -0.003
Connecticut 3,588,683 201 0.011 0.008 -0.003
Iowa 3,160,553 316 0.010 0.012 0.003
Utah 3,159,345 249 0.010 0.010 0.000
Nevada 3,056,824 237 0.009 0.009 0.000
Arkansas 3,020,327 106 0.009 0.004 -0.005
Mississippi 2,982,785 162 0.009 0.006 -0.003
Kansas 2,918,515 267 0.009 0.010 0.002
New Mexico 2,090,708 156 0.006 0.006 0.000
Nebraska 1,932,549 124 0.006 0.005 -0.001
West Virginia 1,803,077 59 0.005 0.002 -0.003
Idaho 1,753,860 195 0.005 0.008 0.002
Hawaii 1,426,393 950 0.004 0.037 0.033
New Hampshire 1,350,575 61 0.004 0.002 -0.002
Maine 1,341,582 594 0.004 0.023 0.019
Montana 1,062,330 299 0.003 0.012 0.008
Rhode Island 1,061,712 63 0.003 0.002 -0.001
Delaware 971,180 179 0.003 0.007 0.004
South Dakota 877,790 160 0.003 0.006 0.004
North Dakota 755,238 100 0.002 0.004 0.002
Alaska 738,068 944 0.002 0.037 0.035
District of Columbia 703,608 154 0.002 0.006 0.004
Vermont 623,960 431 0.002 0.017 0.015
Wyoming 573,720 159 0.002 0.006 0.004
  328,032,421 25,483